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Record W7043103491

RESOLVING THE ROLE OF SUBARCTIC VEGETATION ON MOUNTAIN WATER CYCLING IN A RAPIDLY CHANGING CLIMATE

2023· dissertation· en· W7043103491 on OpenAlexfundaboutno aff

Bibliographic record

VenueMacSphere (McMaster University) · 2023
Typedissertation
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsnot available
FundersGlobal Water Futures
KeywordsSubarctic climateVegetation (pathology)TaigaClimate changeEvapotranspirationMireWater cyclePrecipitationEcosystemEcotone
DOInot available

Abstract

fetched live from OpenAlex

High latitude and altitude ecosystems are currently undergoing rapid and unprecedented warming in response to anthropogenically induced climate change. Subarctic, alpine regions are particularly vulnerable to increases in air temperature and changing precipitation regimes, which have caused cascading hydrological and ecological impacts. In addition to changing flow regimes, thawing permafrost, and declining glaciers, widespread changes in vegetation composition, density and distribution have been observed across northern regions. Specifically, treeline is advancing with increasing latitude and altitude and shrubs are increasing in height, extent, and density. Despite widespread documentation of this northern greening, few field-based studies have evaluated the hydrological implications of these changes. Quantification of total evapotranspiration (ET) across a range of vegetation gradients is essential for predicting water yield, yet challenging in cold alpine catchments due to heterogeneous land cover. Direct field-level measurements of transpiration (T) and evaporative partitioning across subarctic, alpine ecosystems and species are rare, yet essential to assess sensitivities and hydrological response to changing climate drivers. This thesis presents six years of surface energy balance components and ET dynamics and two years of sap flux measurements and critical zone stable water isotope sampling at three sites along an elevational gradient in a subarctic, alpine catchment near Whitehorse, Yukon Territory, Canada. These sites span a gradient of thermal and vegetation regimes, providing a space-for-time comparison for future ecosystem shifts: 1) a low-elevation boreal white spruce forest (~12-20 m), 2) a mid-elevation subalpine taiga comprised of tall, dense willow (Salix) and birch (Betula) shrubs (~1-3 m) and 3) a high-elevation subalpine taiga with short, sparse shrub cover (< 0.75 m) and moss, lichen, and bare rock. We utilize both mass flux measurements and stable water isotopes to evaluate the timing, magnitude, sensitivities, and sources of plant water uptake across these vegetation covers. Total ET decreased and interannual variability increased with elevation, with mean May to September ET totals of 349 (±3) mm at the forest, 249 (±10) mm at the tall, dense shrub site, and 240 (± 26) mm at the short, sparse shrub site. The shrub sites exhibited similar ET losses over 6 years despite differences in shrub height and abundance, although daily rates were higher at the tall shrub site in the peak growing season. From May to September, ET:R ratios were the highest and most variable at the forest (2.19 ± 0.37) and similar at the tall, dense shrub (1.22 ± 0.09) and short, sparse shrub (1.14 ± 0.05) sites. In the mid-growing season, mean T rates were greater at the dense shrub site (2.0 ± 0.75 mm d-1) than the forest (1.47 ± 0.52 mm d-1). During this time, T:ET was lower at the forest (0.48) than at the tall, dense shrub site (0.80). During the growing season between the two years, 2020 was considerably wetter and cooler than 2019. At the tall shrub site, during the mid-growing season (July 1-Aug 15), T dropped considerably in 2020 (-26%), as T was suppressed during the short, wet growing season. In contrast, T at the forest was only moderately suppressed (-3%) between years in this same period. Evapotranspiration was more strongly controlled by air temperature during the early and late season at the forest, while ET at the shrub site was more sensitive to warmer temperatures in the mid-growing season. At the shrub sites, ET was energy limited with no observed soil moisture limitation on T. While 2H and 18O of volume weighted precipitation became more depleted with elevation, the opposite was true in xylem water, where 2H and 18O became more enriched with elevation. Plant water uptake was more reflective of snow water at the forest site than both shrub sites, particularly early in the year and during dry periods. Near-surface bulk soil water had more negative lc-excess at the forest throughout the season and with depth, highlighting increased contributions from soil evaporation. This study combined direct measurements of sap flux, ET, and critical zone isotopes to provide new details on multi-year plant-soil-water dynamics, critical zone water cycling, and species-specific plant water uptake patterns in seasonally frozen soils, which have not previously been reported in cold regions. Our results suggest that advances in treeline will increase overall ET and lower interannual variability; however, the large growing season water deficit and stable water isotope signature at the forest indicates strong reliance on soil moisture from late fall and snowmelt recharge and the potential for plant water stress. Differences between the shrub species were apparent in the sap flux and stable isotope measurements, highlighting the need to further evaluate species specific responses and feedbacks when predicting hydrological fluxes across subarctic ecosystems. Overall, our results suggest that predicted changes in vegetation type and structure in northern regions will have a considerable impact on water partitioning and will vary in a complex way in response to changing precipitation timing, phase and magnitude.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.373
Threshold uncertainty score0.741

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.183
Teacher spread0.177 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2023
Admission routes2
Has abstractyes

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